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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Serval chnages has been made to network architecture as:\n",
    "* For **CFO Network**, it becomes an RNN model (2 Bi-LSTMs layers) with Multi-to-One  architecture.\n",
    "* For **Channel Estimation Network**, it becomes an RNN model (2 Bi-LSTMs layers) with Multi-to-One architecture.\n",
    "* For **Equalization and ECC network**, we merge into one RNN model with 2 Bidirectional LSTMs layers.\n",
    "\n",
    "\n",
    "Interesting notes:\n",
    "\n",
    "* I observe that the CFO Net starts to converge first, then Channel Estimate and eventually the Equalization and ECC. However, if we would like to training end-2-end (without introducing intermediate losses e.g. cfo or channel estimate), the Equalization and ECC net should converge first. This phenomenon indicates that there is room for improving the network architecture.\n",
    "\n",
    "\n",
    "### Network Architecture\n",
    "\n",
    "![](network_architecture.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import multiprocessing as mp\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "from radioml.models import Baseline\n",
    "from radioml.metrics import get_ber_bler\n",
    "from radioml.dataset import RadioDataGenerator\n",
    "from radioml.utils import TrainValTensorBoard\n",
    "\n",
    "from IPython.display import SVG, display\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_LEN = 200\n",
    "PREAMBLE_LEN = 40\n",
    "CHANNEL_LEN = 2\n",
    "\n",
    "SNR_TRAIN = 20.0\n",
    "OMEGA_TRAIN = 1/50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a training generator / validation generator "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "radio = RadioDataGenerator(DATA_LEN,PREAMBLE_LEN, CHANNEL_LEN, modulation_scheme='QPSK')\n",
    "\n",
    "training_generator   = radio.end2end_data_generator(OMEGA_TRAIN, \n",
    "                                                    SNR_TRAIN, \n",
    "                                                    batch_size=128, \n",
    "                                                    num_cpus=16)\n",
    "\n",
    "validation_generator = radio.end2end_data_generator(OMEGA_TRAIN, \n",
    "                                                    SNR_TRAIN, \n",
    "                                                    batch_size=128, \n",
    "                                                    num_cpus=16,\n",
    "                                                    seed=2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Funcs (CFO, Equalization, Demod & ECC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Input, Dense, GRU, LSTM, RepeatVector\n",
    "from tensorflow.keras.layers import Bidirectional, TimeDistributed, BatchNormalization\n",
    "from tensorflow.keras.layers import Lambda\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "def cfo_rnn_network(preamble, preamble_conv, scope=\"CFOEstimationNet\"):\n",
    "    \"\"\"CFO Network (Recurrent Net version)\n",
    "    Arguments:\n",
    "        preamble :     tf.Tensor float32 -  [batch, preamble_length, 2]\n",
    "        preamble_conv: tf.Tensor float32 -  [batch, preamble_length, 2]\n",
    "    Return:\n",
    "        cfo_est: tf.Tensor float32 - [batch_size, 1]\n",
    "    \"\"\"\n",
    "    x = tf.keras.layers.concatenate([preamble, preamble_conv], axis=-1, \n",
    "                                name='Preamble_PreambleConv')\n",
    "    with tf.name_scope(scope):\n",
    "        x = Bidirectional(LSTM(20, return_sequences=True),name=scope+\"_LSTM_1\")(x)\n",
    "        x = Bidirectional(LSTM(20, return_sequences=False),name=scope+\"_LSTM_2\")(x)\n",
    "    cfo_est = Dense(1, 'tanh', name='CFOEstimate')(x)\n",
    "    return cfo_est\n",
    "\n",
    "\n",
    "def channel_estimation_network(preamble, cfo_corrected_preamble, \n",
    "                               scope='ChannelEstimationNet'):\n",
    "    x = tf.keras.layers.concatenate([preamble, cfo_corrected_preamble], \n",
    "                                    axis=-1, name='Preamble_CFOCorrectedPreamble')\n",
    "    with tf.name_scope(scope):\n",
    "        x = Bidirectional(LSTM(20, return_sequences=True),name=scope+\"_LSTM_1\")(x)\n",
    "        x = Bidirectional(LSTM(20, return_sequences=False),name=scope+\"_LSTM_2\")(x)\n",
    "    x = Dense(CHANNEL_LEN, 'sigmoid', name='ChannelEstimate')(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def error_correction_network(cfo_corrected_data, chan_est, scope='Equalizationnet'):\n",
    "    chan_est = RepeatVector(DATA_LEN)(chan_est)\n",
    "    inputs = tf.keras.layers.concatenate([cfo_corrected_data, chan_est], \n",
    "                                         axis=-1,\n",
    "                                        name=\"CFOCorrectedData_ChannelEstimate\")\n",
    "    with tf.name_scope(scope):\n",
    "        x = Bidirectional(LSTM(100, return_sequences=True), \n",
    "                          name=scope+'_BiLSTM_1')(inputs)\n",
    "        x = Bidirectional(LSTM(100, return_sequences=True), \n",
    "                          name=scope+'_BiLSTM_2')(x)\n",
    "    x = TimeDistributed(Dense(1, activation='sigmoid'),\n",
    "                           name='DataEstimate')(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def cfo_correction_func(kwargs):\n",
    "    \"\"\"Rotate packet given an omega estimate \n",
    "    \n",
    "    Arguments:\n",
    "    ----------\n",
    "        omega_estimate: tf.Tensor float32 - \n",
    "                         [batch, 1]\n",
    "        packets:        tf.Tensor float32 - \n",
    "                        [batch, (preamble_len + data_len), 2] \n",
    "    Returns:\n",
    "    --------\n",
    "        rotated_packets: tf.Tensor float32 - \n",
    "                         [batch, (preamble_len + data_len), 2] \n",
    "    \"\"\" \n",
    "    # Because of Lambda Layer, we need to pass arguments as Kwargs\n",
    "    omega_estimate, packets = kwargs[0], kwargs[1]\n",
    "    \n",
    "    with tf.name_scope('rotation_matrix'):\n",
    "        time_step_matrix= tf.range(tf.cast(tf.shape(packets)[1], dtype=tf.float32))\n",
    "        power_term      = tf.complex(0.0, -omega_estimate*time_step_matrix)\n",
    "        rotation_matrix = tf.exp(power_term)\n",
    "        \n",
    "    with tf.name_scope('cfo_correction'):\n",
    "        rotated_packets = tf.complex(packets[..., 0], \n",
    "                                     packets[...,1]) * rotation_matrix\n",
    "        \n",
    "    cfo_corrected_packet = tf.stack([tf.real(rotated_packets), \n",
    "                                     tf.imag(rotated_packets)], \n",
    "                                    axis=-1)\n",
    "    return cfo_corrected_packet\n",
    "\n",
    "def cfo_correction_op(cfo_estimate, packet):\n",
    "    with tf.name_scope('CFO_Correction'):\n",
    "        x   = Lambda(cfo_correction_func, name='CFOCorrection')([cfo_estimate, packet])\n",
    "        # Split packet into [preamble, data]\n",
    "        corrected_preamble = Lambda(lambda x: x[:,:PREAMBLE_LEN,:],\n",
    "                                    name='CFOCorrected_Preamble')(x)\n",
    "        corrected_data     = Lambda(lambda x: x[:,PREAMBLE_LEN:,:], \n",
    "                                    name='CFOCorrected_Data')(x)\n",
    "    return corrected_preamble, corrected_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Combine into a  Gigantic End-to-End Model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "preamble          = Input(shape=(40, 2), name='preamble')\n",
    "corrupted_packets = Input(shape=(240, 2), name='corrupted_packet')  # [preamble_conv, data_conv]\n",
    "\n",
    "\n",
    "preamble_conv = Lambda(lambda x: x[:,:PREAMBLE_LEN, :], \n",
    "                       name='preamble_conv')(corrupted_packets)\n",
    "cfo_est = cfo_rnn_network(preamble, preamble_conv)\n",
    "corrected_preamble, corrected_data = cfo_correction_op(cfo_est, corrupted_packets)\n",
    "chanel_taps     = channel_estimation_network(preamble, corrected_preamble)\n",
    "data_estimates  = error_correction_network(corrected_data, chanel_taps)\n",
    "\n",
    "model = tf.keras.Model(inputs=[preamble, corrupted_packets], \n",
    "                       outputs=[data_estimates, cfo_est, chanel_taps])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training parameters: 352644\n"
     ]
    },
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       "<polyline fill=\"none\" points=\"412.5,-83.5 412.5,-129.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"440\" y=\"-114.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"412.5,-106.5 467.5,-106.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"440\" y=\"-91.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"467.5,-83.5 467.5,-129.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"522.5\" y=\"-114.3\">(None, 200, 200)</text>\n",
       "<polyline fill=\"none\" points=\"467.5,-106.5 577.5,-106.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"522.5\" y=\"-91.3\">(None, 200, 200)</text>\n",
       "</g>\n",
       "<!-- 140309134769904&#45;&gt;140309060668944 -->\n",
       "<g class=\"edge\" id=\"edge20\"><title>140309134769904-&gt;140309060668944</title>\n",
       "<path d=\"M370.5,-166.366C370.5,-158.152 370.5,-148.658 370.5,-139.725\" fill=\"none\" stroke=\"black\"/>\n",
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       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"288\" y=\"-19.8\">DataEstimate: TimeDistributed</text>\n",
       "<polyline fill=\"none\" points=\"382,-0.5 382,-46.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"409.5\" y=\"-31.3\">input:</text>\n",
       "<polyline fill=\"none\" points=\"382,-23.5 437,-23.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"409.5\" y=\"-8.3\">output:</text>\n",
       "<polyline fill=\"none\" points=\"437,-0.5 437,-46.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"492\" y=\"-31.3\">(None, 200, 200)</text>\n",
       "<polyline fill=\"none\" points=\"437,-23.5 547,-23.5 \" stroke=\"black\"/>\n",
       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"492\" y=\"-8.3\">(None, 200, 1)</text>\n",
       "</g>\n",
       "<!-- 140309060668944&#45;&gt;140309060803160 -->\n",
       "<g class=\"edge\" id=\"edge21\"><title>140309060668944-&gt;140309060803160</title>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Number of training parameters: %d\" % model.count_params())\n",
    "SVG(model_to_dot(model, show_shapes=True, show_layer_names=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load modular pretrained weights  and start training\n",
    "\n",
    "* Note: In this epxeriment, we introduce CFO and Channel loss into the total loss. \n",
    "\n",
    "Therefore, given data_length $L$, channel length = 2, the loss function would be:\n",
    "\n",
    "$$L(w, h) = \\frac{1}{L}\\sum^{i=L}_{i=1}(y_i - \\hat{y}_i)^2 + \\frac{1}{2}\\sum^{i=2}_{i=1}(c_i - \\hat{c}_i)^2 + (cfo - \\hat{cfo})^2] $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(tf.keras.optimizers.Adam(0.001), 'mse', loss_weights=[1.0, 1.0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36/50\n",
      "200/200 [==============================] - 281s 1s/step - loss: 3.2093e-04 - DataEstimate_loss: 1.0955e-04 - CFOEstimate_loss: 1.5550e-05 - ChannelEstimate_loss: 1.9583e-04 - val_loss: 2.4212e-04 - val_DataEstimate_loss: 6.2201e-05 - val_CFOEstimate_loss: 8.7319e-06 - val_ChannelEstimate_loss: 1.7119e-04\n",
      "Epoch 37/50\n",
      "200/200 [==============================] - 266s 1s/step - loss: 6.3176e-04 - DataEstimate_loss: 2.5386e-04 - CFOEstimate_loss: 4.2569e-05 - ChannelEstimate_loss: 3.3532e-04 - val_loss: 2.3331e-04 - val_DataEstimate_loss: 5.4928e-05 - val_CFOEstimate_loss: 1.0134e-05 - val_ChannelEstimate_loss: 1.6825e-04\n",
      "Epoch 38/50\n",
      "200/200 [==============================] - 265s 1s/step - loss: 2.5894e-04 - DataEstimate_loss: 9.3495e-05 - CFOEstimate_loss: 1.1844e-05 - ChannelEstimate_loss: 1.5360e-04 - val_loss: 2.4206e-04 - val_DataEstimate_loss: 9.9343e-05 - val_CFOEstimate_loss: 9.8783e-06 - val_ChannelEstimate_loss: 1.3284e-04\n",
      "Epoch 39/50\n",
      "200/200 [==============================] - 265s 1s/step - loss: 2.5141e-04 - DataEstimate_loss: 8.2380e-05 - CFOEstimate_loss: 1.1191e-05 - ChannelEstimate_loss: 1.5784e-04 - val_loss: 2.1898e-04 - val_DataEstimate_loss: 4.8277e-05 - val_CFOEstimate_loss: 8.2481e-06 - val_ChannelEstimate_loss: 1.6245e-04\n",
      "Epoch 40/50\n",
      "200/200 [==============================] - 264s 1s/step - loss: 2.5742e-04 - DataEstimate_loss: 8.4044e-05 - CFOEstimate_loss: 1.0276e-05 - ChannelEstimate_loss: 1.6310e-04 - val_loss: 2.2549e-04 - val_DataEstimate_loss: 5.0048e-05 - val_CFOEstimate_loss: 7.7600e-06 - val_ChannelEstimate_loss: 1.6769e-04\n",
      "Epoch 41/50\n",
      "200/200 [==============================] - 264s 1s/step - loss: 2.4593e-04 - DataEstimate_loss: 8.7216e-05 - CFOEstimate_loss: 1.0095e-05 - ChannelEstimate_loss: 1.4862e-04 - val_loss: 2.3916e-04 - val_DataEstimate_loss: 6.8372e-05 - val_CFOEstimate_loss: 9.5582e-06 - val_ChannelEstimate_loss: 1.6123e-04\n",
      "Epoch 42/50\n",
      "200/200 [==============================] - 265s 1s/step - loss: 2.5261e-04 - DataEstimate_loss: 8.7723e-05 - CFOEstimate_loss: 1.0982e-05 - ChannelEstimate_loss: 1.5390e-04 - val_loss: 1.8924e-04 - val_DataEstimate_loss: 4.7462e-05 - val_CFOEstimate_loss: 8.7832e-06 - val_ChannelEstimate_loss: 1.3299e-04\n",
      "Epoch 43/50\n",
      "200/200 [==============================] - 264s 1s/step - loss: 2.4397e-04 - DataEstimate_loss: 8.0352e-05 - CFOEstimate_loss: 1.1296e-05 - ChannelEstimate_loss: 1.5232e-04 - val_loss: 2.2290e-04 - val_DataEstimate_loss: 7.5006e-05 - val_CFOEstimate_loss: 7.8161e-06 - val_ChannelEstimate_loss: 1.4008e-04\n",
      "Epoch 44/50\n",
      "200/200 [==============================] - 266s 1s/step - loss: 2.5175e-04 - DataEstimate_loss: 8.4282e-05 - CFOEstimate_loss: 1.1837e-05 - ChannelEstimate_loss: 1.5564e-04 - val_loss: 1.8488e-04 - val_DataEstimate_loss: 4.3452e-05 - val_CFOEstimate_loss: 5.2011e-06 - val_ChannelEstimate_loss: 1.3623e-04\n",
      "Epoch 45/50\n",
      "200/200 [==============================] - 266s 1s/step - loss: 2.5121e-04 - DataEstimate_loss: 8.5592e-05 - CFOEstimate_loss: 1.1101e-05 - ChannelEstimate_loss: 1.5451e-04 - val_loss: 2.6417e-04 - val_DataEstimate_loss: 1.0218e-04 - val_CFOEstimate_loss: 6.5674e-06 - val_ChannelEstimate_loss: 1.5543e-04\n",
      "Epoch 46/50\n",
      "200/200 [==============================] - 267s 1s/step - loss: 2.5205e-04 - DataEstimate_loss: 8.6531e-05 - CFOEstimate_loss: 1.0756e-05 - ChannelEstimate_loss: 1.5476e-04 - val_loss: 2.0429e-04 - val_DataEstimate_loss: 4.0509e-05 - val_CFOEstimate_loss: 7.5556e-06 - val_ChannelEstimate_loss: 1.5623e-04\n",
      "Epoch 47/50\n",
      "200/200 [==============================] - 265s 1s/step - loss: 2.5700e-04 - DataEstimate_loss: 8.4061e-05 - CFOEstimate_loss: 1.2552e-05 - ChannelEstimate_loss: 1.6039e-04 - val_loss: 2.3477e-04 - val_DataEstimate_loss: 7.6299e-05 - val_CFOEstimate_loss: 1.2361e-05 - val_ChannelEstimate_loss: 1.4611e-04\n",
      "Epoch 48/50\n",
      "200/200 [==============================] - 265s 1s/step - loss: 2.6964e-04 - DataEstimate_loss: 9.8082e-05 - CFOEstimate_loss: 1.4289e-05 - ChannelEstimate_loss: 1.5727e-04 - val_loss: 3.7535e-04 - val_DataEstimate_loss: 1.0693e-04 - val_CFOEstimate_loss: 3.0974e-05 - val_ChannelEstimate_loss: 2.3745e-04\n",
      "Epoch 49/50\n",
      "200/200 [==============================] - 268s 1s/step - loss: 2.5403e-04 - DataEstimate_loss: 8.5486e-05 - CFOEstimate_loss: 1.3509e-05 - ChannelEstimate_loss: 1.5503e-04 - val_loss: 2.4944e-04 - val_DataEstimate_loss: 8.4489e-05 - val_CFOEstimate_loss: 1.6747e-05 - val_ChannelEstimate_loss: 1.4821e-04\n",
      "Epoch 50/50\n",
      "200/200 [==============================] - 272s 1s/step - loss: 2.3761e-04 - DataEstimate_loss: 8.4714e-05 - CFOEstimate_loss: 9.6754e-06 - ChannelEstimate_loss: 1.4323e-04 - val_loss: 2.4162e-04 - val_DataEstimate_loss: 8.5930e-05 - val_CFOEstimate_loss: 1.1708e-05 - val_ChannelEstimate_loss: 1.4399e-04\n"
     ]
    }
   ],
   "source": [
    "tesorboard = TrainValTensorBoard(\n",
    "    write_graph=False,\n",
    "    log_dir='./logs/{data_len}::{preamble_len}::{snr}::{omega}::end2end_with_intermediate_loss'.format(\n",
    "          data_len=DATA_LEN, \n",
    "          preamble_len=PREAMBLE_LEN, \n",
    "          snr=SNR_TRAIN, \n",
    "          omega=OMEGA_TRAIN))\n",
    "backup_best_model = tf.keras.callbacks.ModelCheckpoint(\n",
    "    '../models/end2end_with_intermediate_losses.hdf5', \n",
    "    save_best_only=True)\n",
    "\n",
    "try:\n",
    "    model.load_weights('../models/end2end_with_intermediate_losses.hdf5')\n",
    "except:\n",
    "    pass\n",
    "\n",
    "history = model.fit_generator(\n",
    "    generator=training_generator,\n",
    "    validation_data=validation_generator,\n",
    "    steps_per_epoch=200,\n",
    "    validation_steps=20,\n",
    "    callbacks=[tesorboard, backup_best_model],\n",
    "    epochs=50,\n",
    "    initial_epoch=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8SFZWFr6+viQnJzdZgr0lu3bt4qmnnuLnn38mMjKSa6+9tk3XqVdfyh1MOXfpthJC1JOWh53V1Fmorj12cWBjISEhjBk7jr/e/XsunXYZYHYE7Nq1K76+vsyfP5+cnJxm7zF27Fjef/99ADZt2sS6desAU8o9ODiY8PBw9u/fz7x58xrOCQ0NbSii2NiYMWP4/PPPKS8vp6ysjNmzZx/TDSaEEE3pvC0PBymvMuMdwX4nr101ffp0pk+byoXvvgfAjBkzmDx5MoMGDWLYsGGkpKQ0e4+bb76Z6667jtTUVPr27cvQoUMBsyNgRkYGKSkpdOvW7ZhS7jfddBOTJk0iISGB+fPnNxzPzMzk2muvZfjw4QDccMMNZGRkSBeVEKJ5WusO+Rg6dKg+3qZNm0441pSSkhKbXteUvMJyvT63SNdZLCd9TZ3FotftKdL5ReVtvk+99sRqL7a+r1prPX/+fMcFYmeeFKvWnhWvJ8WqtWfF255YgVXaxs9Y6bays/LqOgJ9vZstue6lFP6+UqZECOG5JHnYkcWiqaiuO2GKblMCfL2lQKIQwmN1uuRhWmaOUV5TZ4ohnmSwvLEAX69jypR4Kke+n0II99WpkkdAQAAFBQUO+8CrHyw/vixJU+o3iKqs9dzkobWmoKCAgIAAV4cihHCyTjXbKikpidzcXA4ePNjs6yorK9v0gVhwpIpai2Z7acvn1lk0+4srqTrkS4h/2/8ztDVWewkICCApKcll9xdCuEanSh6+vr707NmzxdctWLCAjIyMVl3bYtFkPPYdkwbE8Y+pqS2+XmvNVY99x9kD4njykpZffzJtiVUIYSOtYelzBFR0dXUkbqdTdVs50q8Hj1BcUcPQ5EibXq+UIjU+jM37Tly4J4RwEwe3wnd/JTn7Q1dH4nYkedjJqpxCAIb1sC15AKTEhbFtXyl1Fhl0FsItZZs6b10OLoXKEhcH414kedhJVk4hUcF+9IwJtvmclPhQKmrq2H24vOUXCyGcL3sx+ATgbamGTZ+7Ohq30uGSh1JqslLqleLiYqfeNyunkMzukScto96U1LgwADbnyzcaIdyOxQLZS2DAxZQHJsKa910dkVvpcMlDaz1Ha31TeHi40+556EgVuw6VMczG8Y56fWND8FKwRZKHEO7n4BYoL4DkMeyLOx12L4OCX10dldvocMnDFbLaMN4BZpV5ry4hMmguhDuyjneQPJp9cRNAeUnroxFJHnaQlVOIn7cXAxNb39pJiQuVbish3FH2YojoDpE9qPaPht6nw9oPwCJlhUCSh11k5RQyMDGMAN+WV5YfLzU+jNzCCkoqaxwQmRCiTerHO5LHHj2WfgWU5MGuRa6Ly41I8minypo61ucWMyw5qk3np8abHQu3SdeVEO7jwCaoKITk0UeP9T8PAsKl68pKkkc7bcgrprrOwtBWjnfUS6mfcSXJQwj3kb3E/Nk4efgGwMCpsPkLqHTubE53JMmjneoXB7Y1ecSHBxAW4CPjHkK4k+zFEJkMEd2OPZ4+A2orYeNsl4TlTiR5tFNWTiE9Y4KJCfFv0/lKKVLiw2S6rhDuomG8Y/SJzyVmQkx/6bpCkke7aK35xbo4sD3S4sPYuq8Ui5QpEcL19m+AyqJjB8vrKWUGzvesgEM7nB+bG5Hk0Q67DpVRUFbd6sWBx0uJC6Wsuo7cwgo7RSaEaLOmxjsaGzLduubjPefF5IZsSh5KqWCllJf1535KqQuUUr6ODc39taUYYlNS4s2g+SbpuhLC9bIXQ1QvCE9s+vnQOOhzJqz9sFOv+bC15bEICFBKJQLfAlcBbzkqKE/xS04h4YG+9O4S0q7r9I8NRSnYsk+ShxAuZamDnJ9O3uqol34FlO6FnQucEpY7sjV5KK11OTAFeElrfSkwwHFheYZVOYVkdo/Ay8v2YohNCfTzpmd0MFvyZbquEC61f4OZhps8pvnX9TsHAiI69cC5zclDKTUSmAF8ZT3W+uXUHUhReTU7Dhxp8+LA46XEh7JZWh5CuNauo/WsmuUbAIMuhS1fQkWR4+NyQ7YmjzuB+4HZWuuNSqlewHzHheX+stq5vuN4KXFh5BSUU1ZVa5frCSHaIHsJRPWGsISWX5t+hXXNx2eOj8sN2ZQ8tNYLtdYXaK3/YR04P6S1vt3Bsbm1VTmF+HgphiRF2OV6qdZB8637petKCJew1EHOUujZQpdVvYQM6JLaabuubJ1t9b5SKkwpFQxsADYppe51bGjuLSunkAGJ4QT62af3LiXO1LiScQ8hXGTfOqiyYbyjnlKQMQNyf4aD2xwbmxuytdsqTWtdAlwEzAN6YmZcdUrVtRbW7iliaDsXBzaWFBlIiL+UKRHCZWwd72hs0DRQ3p1yzYetycPXuq7jIuALrXUN0GmXQ2/cW0xVraXdiwMbU0qREhcq03WFcJXsJRDd16zjsFVoLPQ9C9Z91OnWfNiaPF4GsoFgYJFSqgfQaT/l2rpzYEtS48PYkl+K1p02LwvhGnW1ZryjNa2OeulXQGk+/Nq55hDZOmD+nNY6UWt9rjZygAkOjs1tZeUU0i0qkK5hAXa9bkp8KKVVteQVSZkSIZxq31qoLrV9sLyxfudAYFSn67qydcA8XCk1Uym1yvr4P0wrpNPRWrMqp9Cu4x31Gvb2kEFzIZyrvp5Vjza0PHz8rGs+vjIbSHUStnZbvQGUAtOsjxLgTUcF5c72HK7gYGkVQ+20OLCx/g0zrjptj6AQrrFrsSm1HhrbtvPTr4C6Ktgwy75xuTFbk0dvrfVDWuud1scjQC9HBuauVuUcBuw/3gEQ4u9Dj+ggtsiugkI4T10t7F7WtvGOevFDoOuATrXmw9bkUaGUanhnlVKjgE7ZMZ+VU0iovw/9YkMdcv2UuFCZriuEM+WvgeojbRvvqFe/5iMvCw5ssV9sbszW5PE74EWlVLZSKht4Afitw6JyY1k5haR3j8C7ncUQTyYlLoxdBWVUVHeuaX9CuEy2dX1HW8Y7Ghs0Dbx8Os3Aua2zrdZqrYcAg4HBWusMoK9DI3NDxRU1bN1fyrAe9h/vqJcaH4bWsE3KlAjhHLsWQ5cUCOnSvuuEdIG+E82aj7qOX6OuVTsJaq1LrCvNAZ52QDxubfXuQrTGrosDj5cabx00l8WCQjheXQ3sXm57SZKWpF8BR/bDrz/a53purD3b0Dqm38aNZeUU4u2lSO9mn2KITekWGUSQn7dM1xXCGfaugZqy9g2WN9b3bAiK7hRdV+1JHp1uGXRWTiGp8aEE+/s47B5eXor+MmguhHNkLzJ/2it5+PiZsY+tc6H8sH2u6aaaTR5KqfVKqXVNPNYDbZwQ7Zlq6yyssXMxxJNJjQ9jyz4pUyKEw2Uvga5pEBxjv2umXwF11R1+zUdLX6HPd0oUHmBzfinl1XUOWRx4vNS4UN5fsZt9JZXEhwc6/H5CdEq11Wa8I8POBcLjB0PcINN1NfxG+17bjTSbPKw1rASOXRx4vJT4+jIlJZI8hHCUvauhptx+XVaNpc+Ar++D/ZsgNs3+13cD7Rnz6FSycgpJCA8gIcLxH+b1ZUpk0FwIB6of7+gxyv7XHnRph1/zIcnDRlk5hWQ6odUBEBbgS1JkoJQpEcKRspdA7EAIjrb/tYNjoN8kWPexmQ7cAbU6eSilIpVSgx0RjLvKK6ogv7jSKV1W9VLiwmTGlRCOUlsFu1c4psuqXvoMKDsAO35w3D1cyNaS7Ause5hHAb8AryqlZjo2NPexKts63uGEwfJ6qfGh7Dx4hMoaKVMihN3l/QK1FfZbHNiUvmdBUEyH7bqyteURbl1ZPgV4R2s9AjjTcWG1nVJqslLqleLiYrtd85ecQoL8vEmJc0wxxKakxIVh0bDjwBGn3VOITiN7CaCgx2mOu4e3Lwy+DLbOg7ICx93HRWxNHj5KqXjMXh5fOjCedtNaz9Fa3xQeHm63a67KKSSjewQ+3s4bIqovUyJdV0I4QPYiiBsIQQ7uTUi/Aiw1sOFTx97HBWz9NHwU+AbYobX+WSnVC9juuLDcx5GqWjbnlzhlcWBjPaKDCfD1khlXQthbbRXsWenYLqt6cQPNXh8dsOvK1qq6n2itB2utb7H+fafW+hLHhuYe1uwuwqJxyuLAxry9FP1jQ6VAohD2lrsKaisdO1jeWPoMyF8L+zY4535OYuuA+T+tA+a+SqkflFIHlVJXOjo4d7Aq57DZ56W744ohnkxqvJlxJWVKhLAjZ4x3NDZwKnj5drhdBm3ttppoHTA/H8gG+gD3Oiood5KVU0j/2FDCAnydfu+UuFAKy2s4WFrl9HsL0WFlLzblQwKd1BUdHA39J1n3+eg4az5sHjC3/nke8InW2n5TmdxYnUWzencRQ524vqOx+jIlm2TQXAj7qKk04x09xzr3vulXQvkh2P6dc+/rQLYmjy+VUluAocAPSqkuQKXjwnIPW/eVcqSq1qGbPzUnNc4kD1lpLoSd5K2CuirnjXfU63MGBHftUAPntg6Y3wecBgzTWtcAZcCFjgzMHWQ1FEN07mB5vfAgXxLCA9giLQ8h7GPXYlBe0H2kc+/r7QuDp8G2r6HskHPv7SC2Dpj7AlcCHymlPgWuBzreqpfjZOUU0jXUn6RI11W2TbHu7SGEsIPsJRA3GAKdPwGG9BlgqYX1nzj/3g5ga7fVvzFdVi9ZH5nWYx3aqpxChiVHopTrdtxNiQtlx4EjVNVKmRIh2qWmAnJXOr/Lql5sGiRkdJiuK1uTxyla62u01j9aH9cBpzgyMFfbX1JJbmEFmU5eHHi8lPgwai2aXw+UuTQOITxe7s9mhz9nD5Y3lj4D9q2H/HWui8FObE0edUqp3vV/sa4w79BfhVdlFwLOLYbYlDRrmRJZLChEOzWMd5zquhgGXgLefh1izYetyeNeYL61uu5C4EfgbseF5XpZOYUE+HoxICHMpXEkRwfj5+MlNa6EaK/sJRCfDgH2q3vXakFR0P9cWP+x2QbXg9k62+oHoC9wO3Ab0F9rPd+RgblaVs5hhiRF4OvEYohN8fH2ol9siAyaC9Ee1eWm28pV4x2Npc+A8gLY/q2rI2mXZvcwV0pNOclTfZRSaK0/c0BMLldVp9m4t4SbxvZydSiAWe8xf+tBV4chhOfKXWmq27pyvKNe79MhJNZ0XaWe7+po2qzZ5AFMbuY5DXTI5LGzyEKtRbtsceDxUuLD+CQrl4OlVXQJ9Xd1OEJ4nuwloLyh2whXRwLePmafj+UvQcleCEtwdURt0mzysM6qapFS6hqt9dv2Ccn1theZuQCunmlVLzXu6KB5l9AuLo5GCA+0azEkpEOAa8cwGwz7Dax4Geb9ES5719XRtIm9OvTvsNN13MKOIgt9u4YQEeTn6lCAozWutsjeHkK0XnUZ5GU5Z/8OW0X1hAn3w+Y5sOl/ro6mTeyVPFy3is7OLBbNjsI6lxVDbEpUsB+xYf5slum6QrTenhVmvMOdkgfAyNvMave590JFoaujaTV7JY8Os+HEjoNHKK/FrZIHmD3NpeUhRBvUj3e4cn1HU7x94MIXTK2rbx90dTStJi2P47jL4sDjpcSbMiU1dRZXhyKEZ9m1GBIzwT/E1ZGcKH4IjLodVr8Lv3rW6gd7JY+f7HQdl8vKKSTUD5Kjg1wdyjFS48KorrOw86CUKRHCZlVHYO8v7tdl1di4P0FUb5hzhxmf8RC2VtWNVUq9rpSaZ/17mlLq+vrntda/d1SAzpaVc5i+Ed4uLYbYlNT6QXMZ9xDCdntWmEq27rA48GR8A+GC56EoB+b/zdXR2MzWlsdbwDdA/YTkbcCdjgjIlWrrLGT2iCS9q7erQzlBry7B+Hor2VVQiNbIXgxePu6xvqM5yaPM9N3lL0FulqujsYmtySNGa/0xYAHQWtfSAQsj+nh7MXNaOmOTnL9feUt8vb3o0zVUBs2FaI3sJZA41D3HO4535iMQEgdf/N4j6l7ZmjzKlFLRWGdVKaVOBTrFPubuJDUuVLqthLBVVSnk/eLeXVaNBYTB+TPhwCb46RlXR9MiW5PHXcAXQG+l1E/AO5giicKJUuPD2F9SxeEy9/9WIoTL7V4Bus69B8uP1/8cU7Z94T/hwBZXR9MsW5PHRmAcZh/z3wIDAPfKQ8WlAAAgAElEQVT+zTqglPq9PWTcQ4iWZS8CL1/3H+843qR/mG62L24Di/uODtiaPJZprWu11hu11hu01jXAMkcGJk6UEmdmXG2W8uxCtCx7CSQNAz/3mnbfopAuJoHkroSfX3N1NCfVbPJQSsUppYYCgUqpDKVUpvUxHvCw/yKer0uoPzEh/tLyEKIllSWwd43njHccb/A06HMWfP8IFO12dTRNaqkk+9nAtUASMLPR8VLgAQfFJJqRGh8qG0MJ0ZLdy63jHW1PHlprvt98gJcX/kpdRSXbvXYyolcUafFh+Dh6kzilzOD5SyNhzp1w5SxzzI20VJL9beBtpdQlWutZTopJNCMlLpS3l+VQW2dx/P/AQniq7MVmr/Ck4W06fUNeMU98tZllOwvoER1EZYWFJ+ZuBiDYz5uhyVGM6BnF8J5RDE4Kx9/HAWvDIrrDGQ/BvHth3UcwZLr979EOLbU8ANBaz1JKnYcZKA9odPxRRwUmmpYSF0Z1rYXsgjL6dA11dThCuKfsxZDY+vGOfcWVPPXtVmb9kktEoC+PXjiAy4d356fFi0jNPJWVuw43PP71zVYA/H28yOgewfCe0YzoGUVG9wiC/Gz6aG3ZKTfAhk/h6/ug9xlmPMRN2PQbKqX+gxnjmAC8BkwFVjowLnES9WVKNueXSvIQoimVxZC/Fsbea/Mp5dW1vLxwJ68s2kmdRXPTmF7cMqEP4YFHFwzHhgUweUgCk4eYQhuHy6r5OftoMnnhx+08p8HHSzE4KbwhmQxNjiQsoI0Lj728TOmS/4w2G0dd+mbbruMAtqbH07TWg5VS67TWjyil/g+Y58jARNN6dw3Gx0uxOb+k4X9iIUQjOctAW2wa76izaGb9kstT32zlQGkV5w2K50+TUuhuQ2HUqGA/zh4Qx9kD4gAoqawhK6ewIZm8vmQn/1n4K17KfOkb0TOa4daurqjgVmw016U/jP0jzH8cBl0KKefafq4D2Zo8Kqx/liulEoACIN4xIYnm+Pt407tLiAyaC3Ey2YvB27/F8Y6lOw7x+Feb2ZRfQnq3CP59ZSZDe7R9K4awAF8m9O/KhP5dAaiormP1nkJW7DTJ5L0VObzx0y4A+nYNYUzfLtx7dn8C/WwYLxl1B2ycDV/dZepgBYS3OU57sTV5fKmUigD+BfyCKVPivhOQO7iU+FB+3nXY1WEI4Z6yF0PSKeAb0OTTOw4c4cl5m/l+8wESIwJ57vIMJg+Ot3sl7UA/b07rHcNpvWMAqK61sD6viBW7DrNi52He+GkXtRYLj144sOWL+fjBhc/Da2fCdw/BZNeXL7F1wPwx64+zlFJfAgFaa6lt5SKp8WH8b81eistrXB2KEO6logjy15k9Mo5zuKyaZ7/fxrsrdhPo682fJqVw3ahkAnydU0Xbz8eLoT2iGNojilvGw2NfbuL1Jbs4IzWWcf1sGAhPHAqn3gLLXoBBU12+hsXWAXNv4Dwguf4cpRRa65nNnSccIyXODJTLnuZCHGf3MkBDz6P1rKpq63h7aTbP/7iDsqparhjRnTvP7EdMiL/r4gTuPbs/i7Yd5N5P1vLtH8YSEWTDOMiEP8OWL03pkpuXmr1AXMTWhQJzMIsFo4HQRg/hAg0bQ8lKcyGOtcs63pE4DK01X63L58yZC/nb3C0M6xHJN3eO5fGLBrk8cQAE+Hrz9GXpHC6r5s+fb0Br3fJJfkEw+Tk4vBMWPOn4IJth65hHktZ6sEMjETbrGupPZJAvW/aVkhzt6miEcCPZi6HbcFbnV/D4V7+QlVNISlwo/71+OGP6us8aiXoDE8P5w1n9+Nc3W5mYFsuF6Yktn9RrHGRcBUufhwEXQ0K64wNtgq0tj3lKqYkOjUTYTClFanyYFEgUorHyw+h96/mqtA8Xv7SUnIJynpwyiK9uH+OWiaPeb8f2IrN7BH/5fAP5xRUtnwAw8TEIjjEbR9W5ZuzT1uSxHJitlKpQSpUopUqVUtJn4kIpcWFs3VeCxZamrhAdXFVtHZ9/8SkKzXv7u3Pb6X1YcO94pg/vjreXe9WEOl79Dqa1Fs09n6zFYrHh33RgJJz3f7BvvWmBuICtyWMmMBII0lqHaa1DtdZhDoxLtCAlPpTKGgsHyiV5iM5tXW4Rk59fwuENP1Ct/Hjqzt9w98T+hPjbqUSIEyTHBPPgeWn8tKOAt5dl23ZS6mRIvcCMfRza4cjwmmRr8tgDbNA2jegIZ0izDprvKbW4OBIhXKOqto5/fbOFi19aSml5JZeHb8QveSQJMRGuDq1NLh/ejdNTuvLkvC3sOGBjl/S5T5n1LF/cBhbnfhbYmjx2AguUUvcrpe6qfzgyMNG8Pl1D8FKwW5KH6ITW5xZzwfM/8eL8X7k4I5Hvzy0lsGwPnHK9q0NrM6UUT14yiCA/b/7w0Vpq6mz4tx0aC2f/DXYvhSzn1r2yNXnsAn4A/JCpum4hwNebXl1CyJXkITqRqto6nvpmKxe99BNFFdW8ce0wnpo6mOAVz0J0H0g539UhtkvX0AD+PmUQ6/OKef6H7badlD4Deo03K8+L8xwZ3jFsXWH+iKMDEa2XEhfKsm1l1Fm02w8KCsfRWrOvpJIaWwZaPdiGvGLu/ngtW/eXcklmEn89P43wIF/Y8QPsWwcXvABezlkt7kiTBsZzSWYSLy74lQkpXcnoHtn8CUrB+c/Av08zta/if+eUOJtNHkqpZ7TWdyql5mDqWR1Da32BwyITLRqUGM6X6/JJf/Rbhiebap0jekUzMMEJO50Jl9Bas7e4kvW5RazLLWZ9nnkUldfg7w2jdv/M2L4xjO3XhZ4xwXav1+QK1bUWnv9xOy8t+JXoYD/euHYYp6fEHn3BkqchNMFs3dpBPHRBGst3FnDXx2v56vbRLe8PEtUTTn8QvnmArt4DMLtnOFZLLY//Wv98ytGBiNa75rRkCvJ2URrQlRU7D/PDlgPAsTudjegZxeCkCPx8JJl4Gq01+0uqWJdb1JAk1ucWU1BWDZh9I/rFhjJpQBwpcaEsWruNXw8e4Ufr/wdJkYGM69eFsf26cFrvaELbuqeEC23IK+aeT9ayZV8pUzITeej8Aaa1US93lVkYOPEJ8HH9qnF7CQvw5alLh3DFa8v529zNPH7RoJZPGvE72DCLPjteg6o/gL9jRxZa2oY2y/pjutb62cbPKaXuABY6KjDRsgBfb05L8GH8eLP4/0BpJSutFTtX7Cpo2OkswNeLzO6RDfsJZHSPcFoxOGG7A6WVrM89miTW5RVzsLQKAC8F/WJDOT2lK4OTwhmYGE5qfNgx/x2Ta3IYP348OQVlLNp2kIXbDvH56jzeW7EbHy9FZvdIxvYzrZKBCeF4uXFXZ3WthRd+3M6L1tbG69cM44zU2BNfuORpCIiAodc4P0gHG9k7mhtG9+TVxbs4MzWW8dZS7yfl5Q0XvMDWxXMY5ODEAbaXJ7kGePa4Y9c2cUy4UNfQAM4fnMD5g80mUQVHqvg5+3BDCehnftiG1uDn7UV6twhG9DJdXUN7RNpv20xhk4IjVcckifW5xewrqQRMF3afLiGM6RvD4MRwBiVFkBYfZtu+D0CP6GCuGhnMVSOTqa61kJVTyKLtB1m07SBPfbuNp77dRlSwH6P7mEQytm8MXcOaLl/uCse0NjISeWjyca2Nege3miKB4/7k8G/ZrnL3xP4s2naIP366jm/uHEtkS5tIxaZREHPAKbG1NOZxOXAF0FMp9UWjp8IA2VDCzUWH+DNpYDyTBpp9u4rLa6zJpICVuw7z0oJfef7HHfh4KQYlhTOiZzQjekUxrEekR3ZxuBOLRXPoSBW5RRXkFVaQW1hBXlE5eYUVbNt/hLwiU4ZCKegZE8ypvaIYlBTB4KRw0uLDCLbTAjc/Hy9G9o5mZO9o/jQphYOlVSzZcZBF2w6xePtBvli7FzCTL8b178K4vl0YmhyJv4/zW6bVtRZemL+Dl+bvIDLYj9euHsaZaU20Nur99Cz4BMLw3zovSCcL8PVm5mVDuOjFn3jw8w28cEWG24xjtfR/6FIgH4gB/q/R8VJgnaOCEo4RHuTLmWmxDf8gj1TVsqqhZVLAa4uPbps5tEckUzKTOHdQ/DH7OAujts7CvpLKRonBJIm8oqOP6tpjp1FHBPmSGBFIZo9Irj0tmUFJ4QxICHNqou4S6s/FGUlcnJGExaLZlF/Cou0HWbj1IK8v3sXLC3cS6OvNyN7RjO0bw+i+XUiODnL4BIyNe4u555N1bM4vYUpGIn+dnNZ8ifLiXFj3EZxyAwR37OqgAxJM8cR/fr2Vs9bEclGGDcUTnaClMY8cIEcpdSZQobW2KKX6ASnAemcEKBwnxN+H8f27NvSlllfXsnp3Ect3FjB3fT73f7aeh77YyMS0WC7JTGJM35hOM4vLYtHsL7OwZPshcgvLG5JDfUtiX0kldcdNjY0J8ScpMpC0hDAmpsWSFBlIYmQgiRFBJEYGul25DC8vxcBEM35yy/g+HKmqZdmvBSzadpBF2w82DLz7eCmSIgPpHh1Mj6ggukcF0T06iB7R5uf2dHlW11p4cf4OXrS2Nl69ehhnNdfaqLf0BfPnyN+3+d6e5Ldje/Pj5gP85X8bGN4zioQI1+3jUc/W/+qLgDFKqUjgW+Bn4DJghqMCE84X5OfDqD4xjOoTw11n9WNdbjGf/ZLLF2v38uW6fGJC/LkoPYFLhiY17CnS0ew4cITZq3P5fPVe07W0eAVgBqzjwgJIigxieM8oEiNMYkiKDCQxIpCEiECPn4QQ4u/DWWmxDR/eOQVlrNh5mOyCMnIOl7O7oJw1uwspqaw95rwuof4mqUQH0SMq2CSV6CB6RAURFex30m6Wxq2NizMSeail1ka9sgL45W0YNA0iurX79/YE3l6KmdPSOefZRdzzyVrevX6Eyyc82Jo8lNa6XCl1PfCS1vqfSqk1jgxMuJZSiiHdIhjSLYI/n5fG/K0HmJWVy9vLsnltyS5S48O4JDORC9MT6RLq2VMkC45UMWftXj5bnce63GK8FIzu24WzEus4+7RMkiIDiQsPwLeTtLrq9YgOpkd08AnHi8qrySkotyaUsoafl+4o4LOSY1c4h/r70C0qqFFCMcnl8x3VfPntT61rbdRb+QrUlMOoO9r7K3qU7tFB/OX8NO77bD1vLc3mN6N7ujQem5OHUmokpqVRXzzGs79mCZv5+Xhx9oA4zh4Qx+Gyar5ct5dZWbk8/tVm/j5vC+P6dWFKZiJnpsZ6zLfvypo6vt+8n9m/5LFw20FqLZq0+DAePC+VC4Yk0DUsgAULFjCyd8fuT2+LiCA/IoL8GNLtxAKElTV17DlcfmxyOVzO1n2lfL95PzV1R7v6LkpP4OELBtjW2qhXdQRWvgz9z4OuKfb4dTzKZad04/vN+/nH11sY0zeGvrGum2Vma/K4A7gfmK213qiU6gXMd1xYwl1FBftx9chkrh6ZzI4DpXz2Sx6zV+fx+/cPEBrgw/mDE7gkM5GhPSLdZlZIPYtF83P2YWavzuOr9fmUVtYSG+bP9aN7cnFmIilxHbMrzpkCfL3pGxva5IdanUWTX1zB7sPlbFq3lhsuzmj9DX55GyoKYfQf7BCt51FK8fcpgzn7mUX84eM1fHbzKJctALa1ttUizLhH/d93SlVd0adrKH+clMLdE/uzfGcBs7Jy+Xx1Hh+s3E2P6CCmZCQxJTORblFBLo1z58EjzF5tklxuYQVBft5MGhDHlMwkRvaOlrpgTuLtpUiKDCIpMojqPW1oodZWm4HyHqOh2yn2D9BDdAn15+9TBvHb/2bx/I/buXtif5fE0dI6jyVa69HWn/+rtb6q0dMrgUxHBic8g7eXahhof+yiWuZt2Mdnv+TyzA/bePr7bQzvGcXUzCTOGRTntJgOl1U3jGOs3VOEl4JRfWK4e2I/zh4QJ4siPdH6j6F0L1zomp3z3MnZA+KYOjSJF+fvYEJKVzJbKp7oAC39C2o8WjbwuOec9nVNKTUeeAzYCHyotV7grHuL1gn292Hq0CSmDk0ir6iCz1fnMSsrlz/OWsdfv9hAlwDovmM5kUF+RAWbvvOoIF8ig/0ajpmffQn09W5V11dlTR0/bjnAZ7/ksWDrAWotmpS4UP58bioXpCcQ60arqEUrWSyw5BmIGwS9z3B1NG7hoclpLPu1gLs+WsPcO8Y4/QtRS3fTJ/m5qb83SSn1BnA+cEBrPbDR8UmY8ibewGta6ydbiOMIEADk2nJft3LkIGS9ZXb8CkuAsETzZ2g8eHfcBXiJEYHcOqEPt4zvzZo9RXyxdi9rtu+hssbCpr0lFJZXU1RRw8n2p/T38TLJJMiPyGDfkyYcpWDu+n18tW4vJZW1dA315zeje3JxRmKHnVLc6Wz9Cgq2w9Q3zLJ8QWiALzOnDWH6q8t54qvNPHGxDcUT7ail5BGhlLoYs2lUhFJqivW4AsJtvMdbwAvAO/UHlFLewIvAWZhk8LO1/Ik38Pfjzv8NsFhrvVApFYvZT90z1pdoDWs/gG8eMIN8J1AQEmtNKAkQnnRscglLNAnGpxWzUdyQUoqM7pFkdI9kwYKDjB9/WsNzdRZNcUUNheXVFJZVc7is2vxcXnPC3zftLeFweTXFTSScQF9vJg2MY0pmIqf1jpFxjI5Ea1g8EyJ7QuqFro7GrYzoFc1NY3rx8qKdnJkay4SUFoon2pFqbltypVSz+xpqra+z6SZKJQNf1rc8rNN+H9Zan239+/3W6x2fOI6/jh/wvtZ66kmevwm4CSA2Nnbohx9+aEt4Jzhy5AghISFtOrdeQMV++m17iajCNRSHpbK1/61U+UfhX1VAQOUh/KsO4V9VcMKfPnXlJ1yr2jeCKv9oKgNiqPKPoco/2vrowkEdgV+ke5QrsIU93luL1pTVQGm15kiNprpO0yfCmwAf+yYMe8TqTJ4Ub2tijShcR/rav7C13y3kJ5zt4Mia5s7vbY1F88jSCkpr4PFRgajqsjbHOmHChCyt9TBbXttSeRKbkkMbJAJ7Gv09FxhxshdbWzxnAxGYVkyTtNavAK8ADBs2TI8fP75NwS1YsIC2noulDlb8B356HJQXnPsU4cOuZ7iXjdPpKkugNN/U7inZCyV78SvJw69kL6EleVCwBSqLj95O+eB1zpOmxo8HNOfb9d46mSfFCp4Vb6tifecZCIml/6UP0d/XNeNW7v7eJqaUcOGLS5h7IIxpiTgl1hZHWJRSA4F7gQHWQxuBp7TWTqttpbX+DPjMWfdrs/0b4YvbIC8L+p4N5880XVGtERBmHl2amX5XdcQkmJI8Cr98jOi595hNcc5/GvxcOy1WCLvauxp2zoczHzFjhqJJaQlh3D2xP0/O20KS8nPCPoJmLOOklFIXArMxmz79xvpYCHxmfa6t8oDGRWmSrMc8U20V/PgEvDwWCrPhktfhio9anzhs5R8CMX2h13jWD/ozTHjQVBh9/Swo+NUx93RnljpXRyAcZcnT4B8Ow37j6kjc3o1jejE8OYr3NldTWlnj8Pu11JfyKHCW1voNrfU66+MNzED3o+24789AX6VUT+s4xnTgixbOcU+7V8B/xsCif8LAqXDrzzBoqvO6kJQXjLsXrvwUSvLglQmwdZ5z7u0OVr0BT8TBW+fD6ndNt5/oGA7tgE1fwPAbTGtcNMvbS/F/04ZwW0aAU8r8t5Q8fLTW2ccftB6zKTql1AfAMqC/UipXKXW91roW+D3wDbAZ+FhrvbE1gbtcVSnMvRfeONsUaZsxC6a87Lq9BfqcCTcthKhk+GA6/PBYx/5GrjXM/zt8+QdIyDDjQ/+7FZ7qB59eD9u/h7ralq8j3NfSZ82+5CN+5+pIPEa3qCBSo51TX66lMY9apVR3rfXuxgeVUj0Am/5laq0vP8nxucBcm6J0N9u+NR9aJXkw4rdw+l9MV5KrRfaA33wLc++BxU+ZsZdLXu94m+VY6uCru8zamfQrYfIz4OVjxn3WfgAbZsGGT8006EGXwpDpZnGZ8Bwle2HNB2Zv8hDnTT8VtmspeTwEfK+U+huQZT02DLgP+JMjA3NLZYfg6/tg/SfQJQWu/xa6DXd1VMfyDYALX4CkU0zL6JVxMO1tSBzq6sjso6YCZt1g9q4ec7dJ3PVdhN1OMY9Jf4ft38LaD2HFy7DsBYgdCIMvM8kkLN61v4No2bIXQVvgtNtcHYk4iZam6n6ulNoF3A3U/1fcCEzTWq91dHBuQ2tY97FJHFWlMP5+U9XTx433sRh6jfm2/fHV8MYkOPdfkHmNR0znPamKQvjgcti9HM75p2n1NcXHH1Inm0f5YdMSWfcRfPcX+P4h6DXBtEZSzgO/E/erEC5Wfti0KgdeApHJro5GnERLhRF9rEniaifF436KdsOXd8GO78y3+Queh66pro7KNomZZhzksxtgzh2Q+zOc+xT4un4Ly1YrzoN3L4HDv5oSFQOntHwOQFAUDL/RPA7tgHUfmkTy2Y3gFwJpF5oWSfIYsHUtjnCsn1+H6iMw+k5XRyKa0dK/lpX1PyilPKKUpVJqslLqleLi4pZf3BxLnenyePFUyFkKk/4Bv/nGcxJHveBomPEpjP2jmY30+kQzndiTHNhi4i7OhStn2Z44jhfTB05/EG5fC9fONdfZPAfeuQCeGQTfPwwHt9o1dNFK1eWw4t9mnVTsgJZfL1ympeTRuI9jlCMDsRet9Ryt9U3h4baW3jpRUNlu09Uz74/QYyTcuhxO/R14ecYueSfw8obT/wyXfwSFOfDyONj+naujss3uFWZGm6UGrpsLPce2/5peXpA8yrQi79lmWjKxafDTc/DicHhlPCz/D77V7fwCIlpv9btQXtBpN3vyJK2pqtvx1VbDkpkMW/UvM6/84ldg8DTPHidorP8k+O0C+OhqeO9SGH+faZG4a3fNlrnw6XWmQORVnzmm/9s30PStD7wEjhyA9Z+aGVtf/4mRyhdqfzIfZDLI7nh1NbD0Oeh2qvnSJtxaS58aKUqpdUqp9Y1+XqeUWq+UWueMAJ1K18G6jznY5TT4/c8w5LKOkzjqRfUys8QGXwYL/g4fXGYGKN1N1tvw0QzommbidcbAaUhXGHkL/G4x3LyM/bHj4OfX4Ll0+Pp+KN3v+Bg6sw2zoHgPjJFNSj1BSy0PD+vgbyffQLjxRzavWENscIyro3EcvyC4+D9mWuu8+0w3zWX/hfghro7MzGxb9BTMf9xs+jPtHdesoYlNY2vKbcRP+5eJZ8XLsOpNOOV6GHWHrD2wt/rNnrqmQd+Jro5G2KCllocvkKS1zmn8wNSi6pj7eAZGuDoC51DKVOK9bp7pLnh9Iqx+z7UxWerMAsf5j8Pg6aY+mKsXX0b1goteMi3RARfB8pfg2SHw7V/Muh9hH9u/gYObTRdhR2vtd1AtJY9ngKaKBZVYnxOertsp8NtFZrHj/24xU3prq5wfR00lfHKt6SY67Xa46N/utctidG/TWrv1Z7N+ZNkL8Mxg+O4hKCtwdXSerX6zp4juMKCNM+mE07WUPGKbKr1uPZbskIiE84V0gStnm299WW+ZmWaHdzrv/hVFZg3H5i/g7L/BxMfcdxA/pg9MeQVuWQEp58JPz8Kzg+H7R9xz7MgT5CyF3JXmS4N3x+zQ6Iha+hfaXB+OB640Eyfl7QNnPgyXvQuHtsNzGfDCcJj7R9jy1TEbUNlVST68eS7sWWHqcI281TH3sbcu/eCS1+CW5aaPfsnTpiXy4+Mn2XJYnNSSpyEoBjKudHUkohVaSvOrlFI3aq1fbXxQKXUDR2tdiY4kdbIZON84G3YugF/egZUvm9LvCZnQazz0GgfdRrS/PMuh7fDfKVBxGGZ8DL1Pt8Mv4GRdU+DSN2HsvbDwH7DoX2Zw/dSb4dRbOs8YWlvtW2+qN5z+F8+sfNCJtZQ87gRmK6VmcGxhRD/gYkcGJlwooruZUTTKOv6xZyXsWmiSyZKnTcVen0AzF7/nOJNQ4ga3rqspd5VZa+LlDdd+acqqe7LYNFOAct8GWPikSSTL/2NaUqf+DgLavmi1Q1vyNPiFmskbwqO0VBhxP3CaUmoCMNB6+Cut9Y8Oj6yNlFKTgcl9+vRxdSgdg48/9BxjHqc/aLqvsn86mky+f8i8LjDSrP6uTyZRvU4+a2bbt/DJNaZk+lWfmdd2FHEDTddf/jqTQBb8DZa/CCNvM4UcZVOjow7vNC3c026TFpoHsml0Sms9H5jv4FjsQms9B5gzbNiwG10dS4cUEG4GilPONX8vyYddi44mk03/M8fDu5nurZ7jzZ/WdRFx+T/AwhfNh+yMTzvueon4wTD9PchfCwueNNOPl78II39vkoh/qKsjdL2lz5t9WE69xdWRiDaQqQ2ifcLizUr8IZeZKZcFO0wS2bXQFB1c/a55Xdc0iOlHytbPTcvksnc7xwdo/BC4/API+8UkkR8fg23fmCKb7jqjzAn8qgrNuqL0KyA0ztXhiDaQ5CHsRymI6Wsew280i/7y1x5NJtu/Y1/sBOKu+Bh8/FwdrXMlZppJAVlvw5zbzYZiQy5zdVQuk5g3xxS7PO12V4ci2kiSh3AcL2/zoZmYaeoVac2WhQuJ62yJo7GMqyDrTVP+PfX8zrkZVWUxiXnzzF4q0b1dHY1oo87bbhbOJ2UnTFfV2X+H0r2mz7+zKd0Pn9+CT125lF33cJI8hHC2HiMh7SKzOr1kr6ujcY66WrP+5YVhsP1bdva8yj0KcYo2k+QhhCuc9QhYauGHR10diePtWQmvjjebqyUNg1uWs7vHVFdHJdpJkocQrhCZbBYQrv0A8jposYayAvjf7+H1s8zPl74NV34m4xwdhCQPIVxl9F0Q3AW+fsBMc+4oLBaz98kLQ01yPO02+P1KU9Jexr06DEkeQrhKQJhZtb9nOWz63NXR2MfeNaal8eWdZm3P75bAxMc7x5qeTkaShxCulHEVxA6E7/5q9qiydckAAAp8SURBVDTxVBVF8NU98OoEKNoNF78C134FXTvXZqSdiSQPIVzJy9vsYVK02+xS6Gm0hjUfmFlUq16HU240uy4OuUy6qDq4DrdIUAojCo/Taxz0P9fsppc+A0JjXR2RbfZvMtsG5/wEicPgylky/bYT6XAtD631HK31TeHhUgJbeJCJj0NtpSmg6O6qSuGbP8N/RsOBTTD5Obj+O0kcnUyHa3kI4ZGie8Pwm0zX1fCbIG6QqyM6kdamhPo3D0BpPmReDWc8DMHRro5MuECHa3kI4bHG3Wv2Rfn6fvebuntoB/z3Yvj0OjO9+Prv4YLnJXF0YpI8hHAXgZEw4QHIXgxb57o6GqO6HH54DP490pSVP+dfcNMC6HaKqyMTLibJQwh3MvQ6iOkP3z4ItdWujWXfBnhphNl2eMAUuG0VjLjJzBATnZ4kDyHcibcPnP2E2aL151ddF8f+jfDOBaag4bVfwZSXO+6uj6JNJHkI4W76ngV9zoQF/zA1oZxt/yZ4ezJ4+8O1X0LyaOfHINyeJA8h3NHEJ6D6CCz4u3Pve2CLSRxeviZxSBFDcRKSPIRwR11TYNh1sOoN84HuDAe3WhOHjyQO0SJJHkK4q/EPgF+IGTx3tIPb4K3zTUmRa+aYfeiFaIYkDyHcVXA0jPsj7PgOtn/vuPsc2g5vn29+vmYOdOnnuHuJDkOShxDubPhNENULvv2zmflkbwW/mhaHpc6aOPrb/x6iQ5LkIYQ78/Ezda8OboGsN+177YbEUWsSR9cU+15fdGgdLnkopSYrpV4pLi52dShC2Ef/cyF5DMz/G1QU2ueah3eawfG6KrjmC4hNs891RafR4ZKHVNUVHY5SZs+PikJY9FT7r3d4F7w1GWoq4OovIHZA+68pOp0OlzyE6JDiB0PmVbDiZdPd1FaFOabFUVMGV/8P4gbaL0bRqUjyEMJTTHgQfPzh27+07fyi3WaMo6rUJI74wfaNT3QqkjyE8BShsTDmLtj6Fexa1Lpzi/bAW+dBVbE1ccjGTaJ9JHkI4UlOvRXCu8PXD5jptbYozjWJo8KaOBLSHRuj6BQkeQjhSXwD4KxHYP96WP1uy68vzrMmjkK4ejYkZDg+RtEpSPIQwtMMuBi6nQo/Pm7GL06mZK9ZOV5+GK6aDYlDnRej6PAkeQjhaZSCSX+DsgOweGbTrynJN4PjRw7ClZ9B0jDnxig6PEkeQniixKEweDose9FMv22sdJ9pcRzZD1fOki1jhUNI8hDCU53xV1Be8P1DR4+V7jctjpJ8kzi6j3BdfKJDk+QhhKcKT4RRd8DG2bB7Ob7VRWYBYMleuPJT6H6qqyMUHZiPqwMQQrTDqNvhl3dg7r2klxRCTQHM+BR6nObqyMT/t3f3MXZUdRjHvw8thLaYglYrdqvbaINWEGiIQRuNATRVCTUxERENvvxlFKshatFE/zGGqFFEiQZR28QGQipGQgK2KUZNxNfaF6AqBCu0trSNUnwtUB//mFn3st17d2fZu+fO9vkkmztzdrPz3M3M/uacuTNnlkvPI6LNTlkAl3wWDuzk1P88Bu+6DYZXlU4VJ4D0PCLa7px3wN/2sOPIQlYue13pNHGCSM8jou1OOgnesI4nFr6idJI4gaR4REREYykeERHR2KwrHplJMCKi/2Zd8chMghER/TfrikdERPRfikdERDSW4hEREY2leERERGOyXTpDX0g6BPx5wh8c3yLg8DTG6ac2ZYV25W1TVmhX3jZlhXblfTZZX2L7+ZP5wVlbPJ4NSb+x3YrZc9qUFdqVt01ZoV1525QV2pV3prJm2CoiIhpL8YiIiMZSPMZ3U+kADbQpK7Qrb5uyQrvytikrtCvvjGTNNY+IiGgsPY+IiGgsxSMiIhpL8eggabWkP0h6SNK60nl6kbRU0o8lPSDpfklrS2eaiKQ5kn4n6c7SWSYi6XRJmyT9XtJuSa8pnakbSR+r94H7JN0i6dTSmTpJ+o6kg5Lu62h7rqQtkh6sX88omXFEl6xfrPeDnZJ+IOn0khk7jZe343vXSLKkRf3YdopHTdIc4EbgzcAK4ApJK8qm6ulp4BrbK4ALgQ8NeF6AtcDu0iEm6avA3bZfDpzLgOaWtAT4CHCB7bOBOcA7y6Y6znpg9Zi2dcBW28uBrfX6IFjP8Vm3AGfbfhXwR+DamQ7Vw3qOz4ukpcCbgEf6teEUj1GvBh6y/bDtJ4FbgTWFM3Vle7/tbfXy36n+uS0pm6o7SUPAW4GbS2eZiKSFwOuBbwPYftL242VT9TQXmCdpLjAf+EvhPM9g+6fAX8c0rwE21MsbgLfNaKguxstqe7Ptp+vVXwBDMx6siy5/W4CvAJ8A+vaJqBSPUUuARzvW9zLA/4w7SRoGzgd+WTZJT9dT7cz/LR1kEpYBh4Dv1sNsN0taUDrUeGzvA75EdYa5Hzhie3PZVJOy2Pb+evkAsLhkmAbeD9xVOkQvktYA+2zv6Od2UjxaTtJpwPeBj9p+onSe8Ui6FDho+7els0zSXGAl8A3b5wP/ZHCGVZ6hvlawhqrgvQhYIOndZVM14+p+gYG/Z0DSp6mGizeWztKNpPnAp4DP9HtbKR6j9gFLO9aH6raBJelkqsKx0fbtpfP0sAq4TNIequHAiyR9r2yknvYCe22P9OQ2URWTQXQJ8Cfbh2w/BdwOvLZwpsl4TNKZAPXrwcJ5epL0XuBS4EoP9s1xL6U6kdhRH29DwDZJL5zuDaV4jPo1sFzSMkmnUF10vKNwpq4kiWpMfrftL5fO04vta20P2R6m+rveY3tgz45tHwAelXRW3XQx8EDBSL08AlwoaX69T1zMgF7cH+MO4Kp6+SrghwWz9CRpNdWQ62W2/1U6Ty+2d9l+ge3h+njbC6ys9+lpleJRqy+IfRj4EdXBd5vt+8um6mkV8B6qs/jt9ddbSoeaRa4GNkraCZwHfL5wnnHVvaNNwDZgF9UxPVCP0pB0C3AvcJakvZI+AFwHvFHSg1S9p+tKZhzRJevXgecAW+rj7JtFQ3bokndmtj3YPbCIiBhE6XlERERjKR4REdFYikdERDSW4hEREY2leERERGMpHhFTJOlYx8ekt0/nk5glDY/3pNSIQTG3dICIFvu37fNKh4goIT2PiGkmaY+kL0jaJelXkl5Wtw9LuqeeF2KrpBfX7YvreSJ21F8jjxeZI+lb9VwdmyXNK/amIsZI8YiYunljhq0u7/jeEdvnUN2dfH3d9jVgQz0vxEbghrr9BuAnts+leobWyJMNlgM32n4l8Djw9j6/n4hJyx3mEVMk6R+2TxunfQ9wke2H64dXHrD9PEmHgTNtP1W377e9SNIhYMj20Y7fMQxsqSdLQtIngZNtf67/7yxiYul5RPSHuyw3cbRj+Ri5RhkDJMUjoj8u73i9t17+OaNTxF4J/Kxe3gp8EP4/z/vCmQoZMVU5k4mYunmStnes32175OO6Z9RP5D0KXFG3XU01O+HHqWYqfF/dvha4qX4i6jGqQrKfiAGWax4R06y+5nGB7cOls0T0S4atIiKisfQ8IiKisfQ8IiKisRSPiIhoLMUjIiIaS/GIiIjGUjwiIqKx/wFdpeEUaoILsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source: https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/\n",
    "def visualization(history, key):\n",
    "    plt.plot(history.history[key])\n",
    "    plt.plot(history.history['val_%s'%key])\n",
    "    plt.title('%s Loss' % key)\n",
    "    plt.ylabel('%s Loss' % key)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Training', 'Validation'], loc='upper left')\n",
    "    plt.grid()\n",
    "    plt.semilogy()\n",
    "    \n",
    "for key in history.history.keys():\n",
    "    if 'val' not in key:\n",
    "        plt.figure()\n",
    "        visualization(history, key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on multiple $SNRs$ with/without CFO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_SAMPLES = 50000\n",
    "\n",
    "CFOs = [1/50, 1/10000]   # with/without CFO\n",
    "SNR_RANGE = [0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 40.0, 50.0]\n",
    "\n",
    "baseline = Baseline()  # MMSE + Classic Demod + Viterbi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_ber_bler(ber_logs, bler_logs, snr_range, title=''):\n",
    "    \"\"\"Helper function to visualize the result.\"\"\"\n",
    "    \n",
    "    baseline_title = 'Baseline (MMSE +Classic Demod + Viterbi)'\n",
    "    end2nd_title   = 'End-to-End Network'\n",
    "\n",
    "    fig, (left_ax, right_ax) = plt.subplots(1, 2, figsize=(15, 7))\n",
    "    fig.suptitle(title, fontsize=14,)\n",
    "    ### Plot Bit Error Rate (BER) ###\n",
    "    left_ax.plot(snr_range, np.array(ber_logs).T[1, :], 'r-^')\n",
    "    left_ax.plot(snr_range, np.array(ber_logs).T[0, :], 'b--*')\n",
    "    left_ax.legend([end2nd_title,baseline_title], fontsize=10)\n",
    "    left_ax.set_xlabel('SNR (in dB)', fontweight='bold')\n",
    "    left_ax.set_ylabel('Bit Error Rate (BER)', fontweight='bold')\n",
    "    left_ax.grid(True,'both')\n",
    "    left_ax.set_xlim(np.min(snr_range), np.max(snr_range))\n",
    "    left_ax.semilogy()\n",
    "    ### Plot Block Error Rate (BER) ###\n",
    "    right_ax.set_xlim(np.min(snr_range), np.max(snr_range))\n",
    "    right_ax.plot(snr_range, np.array(bler_logs).T[1,:], 'r-^')\n",
    "    right_ax.plot(snr_range, np.array(bler_logs).T[0,:], 'b--*')\n",
    "    right_ax.legend([end2nd_title,baseline_title], fontsize=10)\n",
    "    right_ax.set_xlabel('SNR (in dB)', fontweight='bold')\n",
    "    right_ax.set_ylabel('Block Error Rate (BLER)', fontweight='bold')\n",
    "    right_ax.set_xlim(np.min(snr_range), np.max(snr_range))\n",
    "    right_ax.grid(True,'both')\n",
    "    right_ax.semilogy()\n",
    "    \n",
    "    return fig\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR_dB = 0.000000\n",
      "\t[Modular]  Ber = 0.45020780 | Bler =1.00000000 - 45.71923s\n",
      "\t[Baseline] Ber = 0.46867090 | Bler =1.00000000 - 428.79717s\n",
      "SNR_dB = 5.000000\n",
      "\t[Modular]  Ber = 0.23289790 | Bler =0.99998000 - 44.56653s\n",
      "\t[Baseline] Ber = 0.40761730 | Bler =0.99312000 - 427.01165s\n",
      "SNR_dB = 10.000000\n",
      "\t[Modular]  Ber = 0.00279210 | Bler =0.24914000 - 44.72237s\n",
      "\t[Baseline] Ber = 0.38057950 | Bler =0.94090000 - 426.44853s\n",
      "SNR_dB = 15.000000\n",
      "\t[Modular]  Ber = 0.00018280 | Bler =0.03612000 - 164.18738s\n",
      "\t[Baseline] Ber = 0.37167260 | Bler =0.91026000 - 427.20388s\n",
      "SNR_dB = 20.000000\n",
      "\t[Modular]  Ber = 0.00011830 | Bler =0.02366000 - 44.56593s\n",
      "\t[Baseline] Ber = 0.36819110 | Bler =0.89852000 - 425.77103s\n",
      "SNR_dB = 25.000000\n",
      "\t[Modular]  Ber = 0.00009220 | Bler =0.01844000 - 44.69026s\n",
      "\t[Baseline] Ber = 0.36692330 | Bler =0.89376000 - 426.31997s\n",
      "SNR_dB = 30.000000\n",
      "\t[Modular]  Ber = 0.00008620 | Bler =0.01724000 - 44.86033s\n",
      "\t[Baseline] Ber = 0.36638640 | Bler =0.89228000 - 426.17042s\n",
      "SNR_dB = 40.000000\n",
      "\t[Modular]  Ber = 0.00008460 | Bler =0.01692000 - 44.83907s\n",
      "\t[Baseline] Ber = 0.36608220 | Bler =0.89150000 - 426.00218s\n",
      "SNR_dB = 50.000000\n",
      "\t[Modular]  Ber = 0.00008450 | Bler =0.01690000 - 47.25634s\n",
      "\t[Baseline] Ber = 0.36608270 | Bler =0.89146000 - 426.83765s\n",
      "SNR_dB = 0.000000\n",
      "\t[Modular]  Ber = 0.45013240 | Bler =1.00000000 - 45.70784s\n",
      "\t[Baseline] Ber = 0.31468120 | Bler =1.00000000 - 427.16829s\n",
      "SNR_dB = 5.000000\n",
      "\t[Modular]  Ber = 0.23352590 | Bler =0.99998000 - 44.45770s\n",
      "\t[Baseline] Ber = 0.05959180 | Bler =0.91718000 - 427.02028s\n",
      "SNR_dB = 10.000000\n",
      "\t[Modular]  Ber = 0.00266180 | Bler =0.24190000 - 51.88540s\n",
      "\t[Baseline] Ber = 0.00795210 | Bler =0.46910000 - 431.15446s\n",
      "SNR_dB = 15.000000\n",
      "\t[Modular]  Ber = 0.00017910 | Bler =0.03546000 - 45.36496s\n",
      "\t[Baseline] Ber = 0.00287740 | Bler =0.30730000 - 432.74733s\n",
      "SNR_dB = 20.000000\n",
      "\t[Modular]  Ber = 0.00011250 | Bler =0.02250000 - 50.77634s\n",
      "\t[Baseline] Ber = 0.00206240 | Bler =0.26364000 - 430.22939s\n",
      "SNR_dB = 25.000000\n",
      "\t[Modular]  Ber = 0.00008890 | Bler =0.01778000 - 45.34345s\n",
      "\t[Baseline] Ber = 0.00188590 | Bler =0.25102000 - 430.95883s\n",
      "SNR_dB = 30.000000\n",
      "\t[Modular]  Ber = 0.00008100 | Bler =0.01620000 - 45.11744s\n",
      "\t[Baseline] Ber = 0.00184830 | Bler =0.24806000 - 429.13210s\n",
      "SNR_dB = 40.000000\n",
      "\t[Modular]  Ber = 0.00007790 | Bler =0.01558000 - 47.10895s\n",
      "\t[Baseline] Ber = 0.00183260 | Bler =0.24542000 - 430.26238s\n",
      "SNR_dB = 50.000000\n",
      "\t[Modular]  Ber = 0.00007730 | Bler =0.01546000 - 48.81018s\n",
      "\t[Baseline] Ber = 0.00183920 | Bler =0.24482000 - 427.87123s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for cfo in CFOs:\n",
    "    ber_logs, bler_logs = [], []\n",
    "    for i, snr in enumerate(SNR_RANGE):\n",
    "        print('SNR_dB = %f' % snr)\n",
    "        x_test, y_test = next(radio.end2end_data_generator(cfo, \n",
    "                                                           snr, \n",
    "                                                           batch_size=NUM_SAMPLES, \n",
    "                                                           num_cpus=16, \n",
    "                                                           seed=2019))\n",
    "        Y_test = np.squeeze(y_test[0], -1)  #(batch, 100, 1) to (batch, 100)\n",
    "\n",
    "        # Run Baseline/Neral Receiver\n",
    "        t1 = time.time()\n",
    "\n",
    "        # preprocess inputs for baseline\n",
    "        [preambles, corrupted_packets] = x_test\n",
    "        preambles = preambles.view(complex)\n",
    "        convolved_preamble = np.array(corrupted_packets[:, :radio.preamble_len, :]).view(complex)\n",
    "        convolved_data     = np.array(corrupted_packets[:, radio.preamble_len:, :]).view(complex)\n",
    "        with mp.Pool(mp.cpu_count()) as pool:\n",
    "            baseline_estimate = pool.starmap(baseline, [(i, j , k) for i, j, k \n",
    "                                                        in zip(convolved_data, convolved_preamble, preambles)])\n",
    "\n",
    "        t2 = time.time()\n",
    "\n",
    "        predictions, _, _ = model.predict(x_test, 500)\n",
    "        nn_estimate = np.squeeze(predictions, -1).round()\n",
    "        t3 = time.time()\n",
    "\n",
    "        # Measure BER / BKER for two receivers\n",
    "        ber, bler       = get_ber_bler(np.array(baseline_estimate), Y_test)\n",
    "        nn_ber, nn_bler = get_ber_bler(nn_estimate, Y_test)\n",
    "\n",
    "        print('\\t[Modular]  Ber = {:.8f} | Bler ={:.8f} - {:3.5f}s'.format(nn_ber, nn_bler, t3 - t2))\n",
    "        print('\\t[Baseline] Ber = {:.8f} | Bler ={:.8f} - {:3.5f}s'.format(ber, bler, t2-t1))\n",
    "        ber_logs.append([ber, nn_ber])\n",
    "        bler_logs.append([bler, nn_bler])\n",
    "    \n",
    "    title = 'Comparision between Neural Network and Baseline\\n(Data length = 200, Preamble length = 40'\\\n",
    "        ', $SNR_{train}$ = 20.0 dB)'\n",
    "    \n",
    "    fig = visualize_ber_bler(ber_logs, bler_logs, SNR_RANGE, title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3commpy",
   "language": "python",
   "name": "py3commpy"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
